@@ -21,6 +21,7 @@ Note: the project aims at imitating the well-implemented algorithms in [Deep Lea
2121- SqueezeNet [[ self] ( https://github.com/xiaohu2015/DeepLearning_tutorials/blob/master/CNNs/SqueezeNet.py ) [ paper] ( https://arxiv.org/abs/1602.07360 )]
2222- ResNet [[ self] ( https://github.com/xiaohu2015/DeepLearning_tutorials/blob/master/CNNs/ResNet50.py ) [ caffe ref] ( https://github.com/KaimingHe/deep-residual-networks ) [ paper1] ( https://arxiv.org/abs/1512.03385 ) [ paper2] ( https://arxiv.org/abs/1603.05027 )]
2323- ShuffleNet [[ self] ( https://github.com/xiaohu2015/DeepLearning_tutorials/blob/master/CNNs/ShuffleNet.py ) by pytorch [ paper] ( http://cn.arxiv.org/pdf/1707.01083v2 )]
24+ - DenseNet [[ self] ( https://github.com/xiaohu2015/DeepLearning_tutorials/blob/master/CNNs/densenet.py ) [ pytorch_ref] ( https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py ) [ paper] ( https://arxiv.org/abs/1608.06993 )]
2425
2526### Object detection
2627- YOLOv1 [[ self] ( https://github.com/xiaohu2015/DeepLearning_tutorials/blob/master/ObjectDetections/yolo/yolo_tf.py ) [ paper] ( https://arxiv.org/abs/1506.02640 ) [ ref] ( https://github.com/gliese581gg/YOLO_tensorflow )]
0 commit comments